Apparatus and method for image guided accuracy verification

ABSTRACT

A method includes receiving during a first time interval associated with a path of motion of a dynamic body, image data associated with a plurality of images of the dynamic body. The plurality of images include an indication of a position of a first marker coupled to a garment at a first location, and a position of a second marker coupled to the garment at a second location. The garment is coupled to the dynamic body. During a second time interval, an image from the plurality of images is automatically identified that includes a position of the first marker that is substantially the same as a position of a first localization element relative to the dynamic body and a position of the second marker that is substantially the same as a position of the second localization element relative to the dynamic body.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/957,688, filed on Dec. 3, 2015 (the '688 application). The '688application is a continuation of U.S. application Ser. No. 13/035,945,filed on Feb. 26, 2011, which issued as U.S. Pat. No. 9,218,663 on Dec.22, 2015 (the '945 application). The '945 application is a divisionalapplication of Ser. No. 11/410,143, filed on Apr. 25, 2006, which issuedas U.S. Pat. No. 7,920,909 (the '143 application). The '143 applicationis a continuation-in-part of U.S. patent application Ser. No.11/224,028, filed on Sep. 13, 2005. All of the above applications anddocuments are herein incorporated by reference in their entireties.

BACKGROUND

The invention relates generally to a medical device and particularly toan apparatus and method associated with image guided medical procedures.

Image guided surgery (IGS), also known as image guided intervention(IGI), enhances a physician's ability to locate instruments withinanatomy during a medical procedure. IGS can include 2-dimensional (2-D)and 3-dimensional (3-D) applications.

Existing imaging modalities can capture the movement of dynamic anatomy.Such modalities include electrocardiogram (ECG)-gated orrespiratory-gated magnetic resonance imaging (MRI) devices, ECG-gated orrespiratory-gated computer tomography (CT) devices, and cinematography(CINE) fluoroscopy. The dynamic imaging modalities can capture themovement of anatomy over a periodic cycle of that movement by samplingthe anatomy at several instants during its characteristic movement andthen creating a set of image frames or volumes. Such images can be usedto help a physician navigate a medical instrument to the desiredlocation on the anatomical body during a medical procedure performed onthe anatomical body at a later time.

Typical image-guided medical systems require manual user input toidentify a pre-procedural image that corresponds to the same positionand orientation of an anatomical body during a medical procedure. Thesemanual operations can lead to greater errors and reduced efficiency inimage-guided procedures.

Thus, a need exists for a method and apparatus that can automaticallyidentify pre-procedural images of a targeted anatomical body that can beused to help a physician navigate a medical instrument to a selectedlocation on the anatomical body during a range of motion of theanatomical body.

SUMMARY OF THE INVENTION

Apparatuses and methods for performing gated instrument navigation ondynamic anatomy with automatic image registration are disclosed herein.In one embodiment, a method includes receiving during a first timeinterval image data associated with a plurality of images of a dynamicbody. The plurality of images includes an indication of a position of afirst marker on a garment coupled to the dynamic body and a position ofa second marker on the garment coupled to the dynamic body. The firstmarker is coupled to the garment at a first location, and the secondmarker is coupled to the garment at a second location. The first timeinterval is associated with a path of motion of the dynamic body. Duringa second time interval after the first time interval, data is receivedthat is associated with a position of a first localization elementrelative to the dynamic body, and data is received that is associatedwith a position of a second localization element relative to the dynamicbody. The first localization element is coupled to the garment at thefirst location, and the second localization element is coupled to thegarment at the second location. The second time interval is associatedwith a path of motion of the dynamic body. During the second timeinterval, an image from the plurality of images is automaticallyidentified that includes a position of the first marker that issubstantially the same as the position of the first localization elementrelative to the dynamic body and a position of the second marker that issubstantially the same as the position of the second localizationelement relative to the dynamic body.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is described with reference to the accompanyingdrawings.

FIG. 1 is a schematic illustration of various devices used with a methodaccording to an embodiment of the invention.

FIG. 2 is a schematic illustration of various devices used with a methodaccording to an embodiment of the invention.

FIG. 3 is a front perspective view of an apparatus according to anembodiment of the invention.

FIG. 4 is a graphical representation illustrating the function of anapparatus according to an embodiment of the invention.

FIG. 5 is a schematic illustration of an example of voxels of aconnected-component in a 3-D volume according to an embodiment of theinvention.

FIG. 6 is a schematic illustration of an example of voxels of aconnected-component in a 3-D volume according to an alternativeembodiment of the invention.

FIG. 7 is a flowchart illustrating a method according to an embodimentof the invention.

FIG. 8 is a schematic illustration of the flow of information during anautomatic segmentation process.

DETAILED DESCRIPTION

A method according to an embodiment of the invention includes capturingimages of a dynamic body during a path of motion of the dynamic bodypre-procedurally (also referred to herein as “first time interval”). Theimages can be used to assist a physician in navigating a medicalinstrument to a desired location on the dynamic body during a medicalprocedure performed at a later time (also referred to herein as “secondtime interval”). The method uses a system configured to automaticallyperform segmentation, correlation and registration between data obtainedin “model space” or “image space” (position data taken pre-procedurally)and data obtained in “physical space” (position data obtained during alater medical procedure).

Specifically, an apparatus is configured to be coupled to a selecteddynamic body, such as selected dynamic anatomy of a patient. Dynamicanatomy can be, for example, any portion of the body associated withanatomy that moves during its normal function (e.g., the heart, lungs,kidneys, liver and vasculature). The apparatus can include, for example,two or more markers configured to be coupled to a patient and two ormore localization elements configured to be coupled to the patientproximate the markers. In other embodiments, the apparatus can include,for example, a garment configured to be coupled to a patient, two ormore markers coupled to the garment, and two or more localizationelements coupled to the garment at a location proximate the markers.

A processor, such as a computer, is configured to receive thepre-procedural image data associated with the dynamic body taken duringa pre-surgical or pre-procedural first time interval. The image data caninclude an indication of a position of each of the markers for multipleinstants in time during the first time interval. The processor can alsoreceive position data associated with the localization elements during asecond time interval in which a surgical procedure or other medicalprocedure is being performed. The processor can use the position datareceived from the localization elements and the position data receivedfrom the images to automatically identify an image from thepre-procedural images where the position of the markers at a giveninstant in time during the pre-procedural imaging is substantially thesame as the position of the localization elements corresponding to thosemarkers, at a given instant of time during the later medical procedure.

A physician or other healthcare professional can use the images thatwere automatically identified by the processor during a medicalprocedure performed during the second time interval, such as, forexample, an image-guided medical procedure involving temporalregistration and gated navigation. For example, when a medical procedureis performed on a targeted anatomy of a patient, such as a heart, thephysician may not be able to utilize an imaging device during themedical procedure to guide him to the targeted area within the patient.Markers or fiducials can be positioned or coupled to the patientproximate the targeted anatomy prior to the medical procedure, andpre-procedural images can be taken of the targeted area during a firsttime interval. The markers or fiducials can be viewed within the imagedata, which can include an indication of the position of the markersduring a given path of motion of the targeted anatomy (e.g., the heart)during the first time interval. Such motion can be due, for example, toinspiration (i.e., inhaling) and expiration (i.e., exhaling) of thepatient, or due to the heart beating. During a medical procedure,performed during a second time interval, such as a procedure on a heart,with the markers coupled to the patient at the same location/position asduring the first time interval, the processor receives data from thelocalization elements associated with a position of the localizationelements at a given instant in time during the medical procedure (orsecond time interval).

Because the markers are positioned at the same location on the patientrelative to the dynamic body during both the first time interval and thesecond time interval, and the localization elements are coupled to thepatient proximate the location of the markers, a correlation can be madebetween the position data in image space and the position data inphysical space. For example, a position of the markers at an instant intime during the pre-procedural imaging corresponds to a specificposition and orientation of the dynamic body at an instant in timeduring the path of motion of the dynamic body as viewed in the imagedata. When the medical procedure is performed during the second timeinterval, a position of the localization elements likewise correspondsto a specific positioning of the dynamic body at an instant in timeduring the path of motion of the dynamic body. Although themarker-localization element combinations can move relative to eachother, for example, as the dynamic anatomy moves, the markers are in afixed position relative to the patient during both the first timeinterval and the second time interval. As stated above, the localizationelements are coupled to the patient proximate the markers, thus, whenthe position of the localization elements (identified during the medicalprocedure) is substantially the same as the position of the markers(identified in the image space), the image corresponding to thatposition of the markers is representative of the position of the dynamicbody for that instant during the medical procedure.

An automatic segmentation-correlation-registration process can beperformed after the image dataset is imported into the processor and thelocalization elements are connected to the processor. Once performed,the correlation does not change during the course of the procedure andthe model space marker positions provide a baseline position for thetemporal registration. After the segmentation-correlation and baselineregistration has been computed, the localization element locations aresampled automatically and continuously to determine when the dynamicbody is at or near the position at which the images were acquired. Theaffine rigid-body transformation is computed automatically andcontinuously until a temporal gating threshold is exceeded, indicatingthat the dynamic body is no longer near the same configuration as wherethe images were acquired. The automatic process produces simulatedreal-time, intra-procedural images illustrating the orientation andshape of the targeted anatomy as a catheter or similar structure isnavigated to the targeted anatomy. Thus, during the medical procedure,the physician can view selected image(s) of the targeted anatomy thatcorrespond to and simulate real-time movement of the anatomy.

In addition, during a medical procedure being performed during thesecond time interval, such as navigating an instrument, such as acatheter or needle to a targeted anatomy, the location(s) of anelectromagnetic coil coupled to the instrumentation during the secondtime interval can be superimposed on an image of the instrumentation.The superimposed image(s) of the instrument can then be superimposed onthe selected image(s) from the first time interval, providing simulatedreal time imaging of the instrument location relative to the targetedanatomy. This process and other related methods are described in pendingU.S. patent application Ser. No. 10/273,598, entitled Methods,Apparatuses, and Systems Useful in Conducting Image GuidedInterventions, filed Nov. 8, 2003, the entire disclosure of which isincorporated herein by reference.

Having described above various general principles, several exampleembodiments of these concepts are now described. These embodiments areonly examples, and many other embodiments are contemplated by theprinciples of the invention, and will be apparent to the artisan in viewof the general principles described above and the exemplary embodiments.For example, other possible embodiments can be used to perform some orall of the functions described herein, such as those systems and methodsdescribed in U.S. patent application Ser. No. 11/224,028, filed Sep. 13,2005, entitled “Apparatus and Method for Image Guided AccuracyVerification” (referred to herein as “the '028 application”), thedisclosure of which is hereby incorporated by reference in its entirety.

FIGS. 1 and 2 are schematic illustrations of devices that can be used toperform various procedures described herein. An apparatus 10 includestwo or more markers or fiducials 22 coupled to a dynamic body B atselected locations, as shown in FIG. 1. The dynamic body B can be, forexample, a selected dynamic portion of the anatomy of a patient. Themarkers 22 are constructed of a material that can be viewed on an image,such as an X-ray. The markers 22 can be, for example, radiopaque, andcan be coupled to the dynamic body B using known methods of couplingsuch devices to a patient, such as with adhesive, straps, etc. FIGS. 1and 2 illustrate the apparatus 10 having four markers 22, but any numberof two or more markers can be used.

An imaging device 40 can be used to take images of the dynamic body Bwhile the markers 22 are coupled to the dynamic body B, pre-procedurallyduring a first time interval. As stated above, the markers 22 arevisible on the images and can provide an indication of a position ofeach of the markers 22 during the first time interval. The position ofthe markers 22 at given instants in time through a path of motion of thedynamic body B can be illustrated with the images. The imaging device 40can be, for example, a computed tomography (CT) device (e.g.,respiratory-gated CT device, ECG-gated CT device), a magnetic resonanceimaging (MRI) device (e.g., respiratory-gated MRI device, ECG-gated MRIdevice), an X-ray device, or any other suitable medical imaging device.In one embodiment, the imaging device 40 is a computedtomography—positron emission tomography device that produces a fusedcomputed tomography—positron emission tomography image dataset. Theimaging device 40 can be in communication with a processor 30 and send,transfer, copy and/or provide image data taken during the first timeinterval associated with the dynamic body B to the processor 30.

The processor 30 includes a processor-readable medium storing coderepresenting instructions to cause the processor 30 to perform aprocess. The processor 30 can be, for example, a commercially availablepersonal computer, or a less complex computing or processing device thatis dedicated to performing one or more specific tasks. For example, theprocessor 30 can be a terminal dedicated to providing an interactivegraphical user interface (GUI). The processor 30, according to one ormore embodiments of the invention, can be a commercially availablemicroprocessor. Alternatively, the processor 30 can be anapplication-specific integrated circuit (ASIC) or a combination ofASICs, which are designed to achieve one or more specific functions, orenable one or more specific devices or applications. In yet anotherembodiment, the processor 30 can be an analog or digital circuit, or acombination of multiple circuits.

The processor 30 can include a memory component 32. The memory component32 can include one or more types of memory. For example, the memorycomponent 32 can include a read only memory (ROM) component and a randomaccess memory (RAM) component. The memory component can also includeother types of memory that are suitable for storing data in a formretrievable by the processor 30. For example, electronicallyprogrammable read only memory (EPROM), erasable electronicallyprogrammable read only memory (EEPROM), flash memory, as well as othersuitable forms of memory can be included within the memory component.The processor 30 can also include a variety of other components, such asfor example, co-processors, graphic processors, etc., depending upon thedesired functionality of the code.

The processor 30 can store data in the memory component 32 or retrievedata previously stored in the memory component 32. The components of theprocessor 30 can communicate with devices external to the processor 30by way of an input/output (I/O) component (not shown). According to oneor more embodiments of the invention, the I/O component can include avariety of suitable communication interfaces. For example, the I/Ocomponent can include, for example, wired connections, such as standardserial ports, parallel ports, universal serial bus (USB) ports, S-videoports, local area network (LAN) ports, small computer system interface(SCSI) ports, and so forth. Additionally, the I/O component can include,for example, wireless connections, such as infrared ports, opticalports, Bluetooth® wireless ports, wireless LAN ports, or the like.

The processor 30 can be connected to a network, which may be any form ofinterconnecting network including an intranet, such as a local or widearea network, or an extranet, such as the World Wide Web or theInternet. The network can be physically implemented on a wireless orwired network, on leased or dedicated lines, including a virtual privatenetwork (VPN).

As stated above, the processor 30 can receive image data (also referredto herein as “image dataset”) from the imaging device 40. The processor30 can identify the position of selected markers 22 within the imagedata or voxel space using various segmentation techniques, such asHounsfield unit thresholding, convolution, connected component, or othercombinatory image processing and segmentation techniques. The processor30 can determine a distance and direction between the position of anytwo markers 22 during multiple instants in time during the first timeinterval, and store the image data, as well as the position and distancedata, within the memory component 32. Multiple images can be producedproviding a visual image at multiple instants in time through the pathof motion of the dynamic body. The processor 30 can also include areceiving device or localization device 34, which is described in moredetail below.

As shown in FIG. 2, during a second time interval, two or morelocalization elements 24 are coupled to the markers 22 for use during amedical procedure to be performed during the second time interval. Thelocalization elements 24 are coupled to the patient adjacent the markers22. The localization elements 24 can be, for example, electromagneticcoils, infrared light emitting diodes, and/or optical passive reflectivemarkers. The markers 22 can include plastic or non-ferrous fixtures ordovetails or other suitable connectors used to couple the localizationelements 24 to the markers 22. A medical procedure can then be performedwith the markers 22 coupled to the dynamic body B at the same locationas during the first time interval when the pre-procedural images weretaken. During the medical procedure, the localization elements 24 are incommunication or coupled to the localization device 34 included withinprocessor 30. The localization device 34 can be, for example, an analogto digital converter that measures voltages induced onto localizationcoils in the field; creates a digital voltage reading; and maps thatvoltage reading to a metric positional measurement based on acharacterized volume of voltages to millimeters from a fixed fieldemitter. Position data associated with the localization elements 24 canbe transmitted or sent to the localization device 34 continuously duringthe medical procedure during the second time interval. Thus, theposition of the localization elements 24 can be captured at giveninstants in time during the second time interval.

The image dataset, the position data for the markers from the first timeinterval (“model space”) and the position data for the localizationelements during the second time interval (“physical space”) can be usedto perform an automatic segmentation, correlation and registrationbetween the data in the model space and the data in the physical space.The result of the analysis is to provide a physician with images thatrepresent the position of a dynamic body during the second time intervalwhen the physician is performing a medical procedure on the dynamicbody. The processor 30 can be configured to perform the automaticsegmentation-correlation-registration process as described in moredetail below.

To identify actual position data associated with the markers 22 withinthe image dataset, the processor 30 can perform an automatedsegmentation procedure. Segmentation is the process of identifyingreference points in the 3-D image dataset. The purpose of thesegmentation is to automatically locate potential “landmarks” in thedataset that indicate a location where a marker 22 may be located.Segmentation can be performed in a variety of different manners. Forexample, a segmentation process can include, intensity filtering,connectivity analysis, and size and shape filtering to identifycandidate sensor (e.g., marker) locations, or model space (also referredto herein as “image space”) coordinates of the marker 20 candidates. Insome example embodiments, the intensity filtering applies domainknowledge to threshold the 3-D image dataset to select only those imagevalues that fall within a designated intensity range that contains thereference points. For example, reference markers can be designated toappear in CT scans with Hounsfield units higher than the anatomicalstructures within the 3-D image. An example output from an intensityfiltering process can include a 3-D binary volume with non-zero entriesindicating voxels (i.e., a 3-D data point) with an intensity that fallswithin the range of values that characterize an image marker, asillustrated in FIG. 8. FIG. 8 is a schematic illustration of the flow ofinformation during one example of an automatic segmentation process.

After filtering the image values based on intensity, a connectivityanalysis can be performed. A connectivity analysis can use the outputfrom the intensity filtering to analyze the potential candidatesidentified in the 3-D image dataset to identify, for example,“connected-components.” A connected-component, as used here, is acontinuous 3-D region in the 3-D volume (i.e., image dataset) that isconnected via adjacent voxels that are in contact with one another.Examples of voxels of connected-components are illustrated in FIGS. 5and 6. FIG. 5 illustrates a connected-component having 8 connected voxelelements (indicated by the shaded boxes), and FIG. 6 illustrates aconnected component having 4 connected voxel elements (indicated by theshaded boxes). From the identified connected-components, informationabout the connected regions, such as the location of each voxel element,the geometric center, and the volume and bounding perimeter dimensionscan be identified. An example output of a connectivity analysis caninclude, for example, a list of each separate connected-component in the3-D binary volume, and can be used in the next step in the segmentationprocess.

Next, in some embodiments, the output from the connectivity analysis,which includes the identified connected-components, can be filteredbased on size and shape criteria during a size threshold analysis.First, knowledge about the size of the reference markers can be used toidentify and discard any connected-components that are too small or toolarge to be valid markers. A list of connected-components that fulfillthe size criteria can then be evaluated based on the shape of thecomponents during a shape-filtering analysis. Knowledge about the shapeof the reference markers can be used to discard any components that donot match the known shape of the reference markers. For example, if themarkers are known to be cylindrical, then the connected component shapecan be analyzed to determine if the ratio between the major axis and theminor axis is within a set criteria. The output from this step in thisexample process includes, for example, a list of connected-componentsthat fulfill the shape criteria. Other analysis can be performeddepending on the particular marker configuration, such as, for example,checking whether the connected-component shape is symmetric about acentroid of the connected-component.

After the segmentation process is performed, an automatic correlationprocess can be performed. Correlation as used here is the process ofcorrectly matching reference points between the image or model space andthe physical space. Correctly matching the reference points aids inaccurately computing the registration between the data in the imagespace and the data in the physical space without user interaction. Thecorrelation process determines where each of the localization elementsis positioned in the model images. Correct correlation is required tocompute an affine transform between model space and physical space. Theapparatuses and methods described herein enable the process to beautomated with minimal user intervention. Automatic correlation resultsin an understanding of the location of the markers in image space andphysical space, as well as the corresponding labeling/identification ofeach marker in each space.

Because there are a large number of possible solutions, computations ofall possible combinations can result in long computation times.According to an embodiment of the invention, the processor 30 can beconfigured to compute the correlation between the image space and thephysical space at a much faster rate (e.g., 2 seconds on a 1.5 GHz G4Macintosh computer).

Because the number of localization element positions in the physicalspace is typically smaller than the number of identified markerpositions in the model space, a guess at a correlation can be made forthree localization element points in physical space. An affine transformregistration is then computed between the selected positions of thelocalization elements 24 in physical space and the model space. Thecomputed registration is then used to transform the remaininglocalization element positions to model space and determine if anymarkers exist at the projected locations. A brute force iteration ismade in groups of 3 as just described. When projecting the remainingpoints from physical space to model space to test the correlation guess,a test can be performed for the existence of a marker in model spacewithin a settable threshold 3-D distance. If present, a 3-D error can becomputed and the correlation resulting in the lowest error can be notedand recorded. This technique discards points in model space that do nothave a corresponding point in physical space (i.e., false positives inthe list of marker positions determined during segmentation).

Because the number of localization element positions is relatively low,it can be fairly computationally inexpensive to perform the iterativeprocess described above to search all possible correlation combinations.The process is implemented such that the affine transform used tocompute rigid body registration between the model space and the physicalspace for each 3-point correlation is abstract, and the actualimplementation can be defined and determined at runtime. It is possibleto improve the speed of the process by stopping the solution searchiterations if a solution is identified that meets the specifiedcriteria. For example, when computing the error for a correlation guess,the projection loop-and-fail for the correlation guess can be reduced ifany single reference point in the physical space fails to map to a pointin model space within a specified error threshold. Each potentialcorrelation combination is evaluated by one or more criteria todetermine the correlation between segmented markers in model space andphysical localization element locations. Examples of evaluation criteriainclude computing the transformation using three points, and thenprojecting the remaining physical points to model space as describedpreviously. Other examples include incorporating coil orientationinformation between the segmented markers and 5- or 6-degrees of freedom(DOF) localization elements, or applying externally availableinformation, such as requiring the user to attach the localizationelements in a certain configuration. This correlation technique canaccount for physical localization elements being in a slightly differentrelative position than the model space markers since the localizationelements process can be performed when the localization elements areanywhere in the periodic cycle of the dynamic body.

After the correlation process, the processor 30 can perform an automaticregistration process. The process of registration tracks temporalmovement of the dynamic body via the movement of the markers 22, andwhen temporally valid, computes the transformation between the physicalspace and the model space.

A measure of a temporal position is referred to herein as a“cost-function.” An automatic registration algorithm uses abstractobjects so that the cost-function can be defined/determined at runtime.For example, one possible cost function is the average distance betweenreference points (e.g., positions of localization elements 24).Cost-functions can compute a temporal measure for a group of referencepoints independent of the space in which the points are known since themeasure is based upon landmark positions relative to each other. Oncethe correlation is established, the localization element locations inphysical space can be periodically evaluated using a cost-function todetermine when the dynamic body most closely matches the point in theperiodic phase of the first time interval (image acquisition). Examplesof cost-functions can include: average distance between markers; max/minaxis ratio of bounding ellipsoid; and a ratio between minimum andmaximum 3D distance between markers. The cost-function can be, forexample, determined in patient model space to ensure that moving thepatient and/or localizing machinery will not affect theoutcome/solution/computation.

A cost-function can be used to establish a measure of the markerpositions within the plurality of images during the first time interval.The same cost-function can then be applied continuously to thecorrelated localization element positions during the second timeinterval. When the cost-function indicates that the positions of thelocalization elements in the second time interval have the same relativepositions as the marker positions in the first time interval, then thedynamic body can be identified as being at the same temporal point alongthe path of motion as the first time interval. During the time that thecost-function indicates that the dynamic body is at the same temporalpoint along the path of motion as the first time interval, then theautomatically correlated markers from the first time interval andlocalization elements from the second time interval can be used toautomatically compute a registration. When the cost-function indicatesthat the registration is valid, then the position and navigational pathof a medical instrument can be displayed on a computer screensuperimposed onto images of the dynamic body acquired during the firsttime interval.

After performing the automated segmentation and correlation processes, alist of position data for the localization elements 24 in image space isobtained. This represents the position of the markers 22, and thereforethe position of the dynamic body B when the image dataset was acquired.This information is used as the “temporal reference” for the imagedataset and represents the nominal reference point position for thedataset. For multiple images acquired at different points in the patienttemporal cycle (e.g., at inspiration and expiration of the respiratorycycle), the segmentation-correlation process can be repeated and atemporal reference position can be determined for each image.

Once the temporal reference is established for each image dataset, aregistration filter can be used to compare the position of thelocalization elements 24 in the physical space to the temporal referencelocation for each image dataset. If the positions of the localizationelements 24 are sufficiently close to the temporal reference for adataset (i.e., the image dataset), then the dataset can be used fornavigation for that temporal moment by computing the affinetransformation between the physical space and model space. Thetransformation is then used to project information such as reformattedimages, segmentations, informatics, etc. The threshold that determineshow close the physical configuration must be to the locations in theimage dataset can be modified at runtime to allow the sensitivity ortemporal resolution to be modified.

Through the automatic registration process, the relative markerpositions at the time of the 3-D scan can be determined. Thisacquisition of relative marker position allows the point in therespiratory cycle at which the scan was acquired to be determined andnavigation gated to that same point in the cycle during a subsequentmedical procedure. The resulting registration is relative to the markersaffixed to the patient, which allows the patient to be repositionedrelative to the scan gantry, table position, and/or localizationmachinery without invalidating the registration, as long as the markersremain in a fixed position relative to the patient.

As stated previously, the automaticsegmentation-correlation-registration process can be performed using anapparatus that includes a garment, such as a garment disclosed in the'028 application. Such an apparatus can be used with the systems andmethods described herein to perform the sameautomatic-segmentation-registration processes described above, except insuch an embodiment, the markers and localization elements are coupled tothe patient through the use of a garment. All other devices describedwith reference to FIGS. 1 and 2 can be used in this embodiment toperform the same automatic segmentation-correlation-registrationprocesses as described above.

FIG. 3 illustrates an apparatus 210 that includes a garment 220 that istubular shaped and can be constructed with a flexible and/or stretchablematerial. This particular garment configuration is only one example of agarment that can be used. It should be understood that other garmentconfigurations can alternatively be used, such as those described in the'028 application. The apparatus 210 further includes multiple markers orfiducials 222 coupled to the garment 220 at spaced locations. Aplurality of localization elements 224 are removably coupled proximateto the locations of markers 222, such that during a first time intervalas described above, images can be taken without the elements 224 beingcoupled to the garment 220. In other embodiments, the localizationelements 224 need not be removably coupled to the markers 222. Forexample, the localization elements 224 can be fixedly coupled to thegarment 220. In addition, the localization elements 224 can be coupledto the garment 220 during the pre-procedure imaging.

The garment 220 can be positioned over a portion of a patient's body(proximate dynamic anatomy), such as around the upper or lower torso ofthe patient at a fixed location relative to the patient during both afirst time period, in which images are taken of the dynamic anatomy(model or image space), and during a second time period, in which amedical procedure is being performed on the dynamic anatomy (physicalspace). The stretchability of the garment 220 allows the garment 220 toat least partially constrict some of the movement of the portion of thebody for which it is coupled. The markers 222 are coupled to the garment220 at a fixed location on the garment 220, thus the markers 222 arealso coupled to the patient at a fixed location relative to the dynamicanatomy during both the first time period and the second time period.

FIG. 4 is a graphical illustration indicating how the apparatus 210(shown without localization elements 224) can move and changeorientation and shape during movement of a dynamic body, such as amammalian body M. The graph is one example of how the lung volume canchange during inhalation (inspiration) and exhalation (expiration) ofthe mammalian body M. The corresponding changes in shape and orientationof the apparatus 210 during inhalation and exhalation are alsoillustrated. Although FIG. 4 is being described with reference to anembodiment including a garment, an embodiment that does not include agarment can be similarly described. The six markers 222 shown in FIG. 3are labeled a, b, c, d, e, and f. As described above, images of thedynamic anatomy with the apparatus 210 coupled thereto can be takenduring a first time interval. The images can include an indication ofrelative position of each of the markers 222, that is the markers 222are visible in the images, and the position of each marker 222 can thenbe identified over a period of time. As illustrated, during expirationof the mammalian body M at times indicated as A and C, a distance Xbetween markers a and b is smaller than during inspiration of themammalian body M, at the time indicated as B. Likewise, a distance Ybetween markers b and f is greater during inspiration than duringexpiration.

FIG. 7 is a flowchart illustrating a method according to anotherembodiment of the invention. A method includes at step 80 receivingduring a first time interval image data associated with a plurality ofimages of a dynamic body. The plurality of images include an indicationof a position of a first marker on a garment coupled to the dynamic bodyand a position of a second marker on the garment coupled to the dynamicbody. The first marker is coupled to the garment at a first location andthe second marker is coupled to the garment at a second location. Thefirst time interval is associated with a path of motion of the dynamicbody. At step 82, data associated with a position of a firstlocalization element relative to the dynamic body is received, and dataassociated with a position of a second localization element relative tothe dynamic body is received during a second time interval after thefirst time interval. The first localization element is coupled to thegarment at the first location, and the second localization element iscoupled to the garment at the second location. The second time intervalis associated with a path of motion of the dynamic body and the garmentis coupled to the dynamic body in a fixed position relative to thedynamic body during both the first time interval and the second timeinterval.

During the second time interval, an image from the plurality of imagesassociated with a position of the first marker that is substantially thesame as the position of the first localization element relative to thedynamic body and a position of the second marker that is substantiallythe same as the position of the second localization element relative tothe dynamic body are automatically identified at step 84. The automaticidentification can be based on an appearance of the markers within theidentified image. The automatic identification can also includeidentifying a position of a third localization element, and projectingthat position on to the image data set and determining whether a thirdmarker exists in an image from the image data set at the position of thethird localization element. The automatic identification can alsoinclude correlating a position of the first localization element duringthe second time interval with the position of the first marker in theplurality of images. At step 86, the path of motion of the dynamic bodyis automatically registered during the second time interval isautomatically registered with the path of motion of the dynamic bodyduring the first time interval. The automatic registering in step 86 caninclude identifying at least one temporal reference within the pluralityof images and identifying whether the at least one temporal reference isassociated with at least one of the first marker or the second markerproviding a navigational path for a medical instrument to be directedbased on the identified image.

At step 88, a navigational path is provided for a medical instrument tobe directed based on the identified image. A physician can use thenavigational path to guide a medical instrument to the dynamic bodywhile performing a medical procedure on the dynamic body during thesecond time interval.

CONCLUSION

While various embodiments of the invention have been described above, itshould be understood that they have been presented by way of exampleonly, and not limitation. Thus, the breadth and scope of the inventionshould not be limited by any of the above-described embodiments, butshould be defined only in accordance with the following claims and theirequivalents.

The previous description of the embodiments is provided to enable anyperson skilled in the art to make or use the invention. While theinvention has been particularly shown and described with reference toembodiments thereof, it will be understood by those skilled in art thatvarious changes in form and details may be made therein withoutdeparting from the spirit and scope of the invention. For example, thegarment, markers and localization elements can be constructed from anysuitable material, and can be a variety of different shapes and sizes,not necessarily specifically illustrated, while still remaining withinthe scope of the invention.

While a relatively small number of markers are discussed, the system isscalable and the use of any number of markers is contemplated. Forexample, a garment may include between 2 and 20 markers, 10-50 markers,etc. Additionally, variations in the automated processes can be used toachieve the same, or substantially the same, desired results.

What is claimed is:
 1. A method for imaging a dynamic body of a patienthaving predictable movement during a periodic cycle, comprising: a)attaching a first marker to a first location on the patient, and asecond marker to a second location on the patient that moves differentlythan the first location during the periodic cycle; b) receiving from animaging device a plurality of images of the dynamic body over theperiodic cycle taken during a first time interval prior to a medicalprocedure, each image showing a position of the first marker and aposition of the second marker; c) attaching a first localization elementto the first location on the patient and a second localization elementis to the second location on the patient; d) receiving, at a second timethat is during the medical procedure, data identifying positions for thefirst and second localization elements; and e) automatically identifyingfor the second time an identified image from the plurality of images,the identified image being selected to show the first marker atsubstantially the same as the position of the first localization elementrelative to the dynamic body and showing the position of the secondmarker at substantially the same as the position of the secondlocalization element relative to the dynamic body.
 2. The method ofclaim 1, further comprising displaying, on an image display theidentified image for use in navigating a medical instrument during themedical procedure.
 3. The method of claim 1, a third marker and a thirdlocalization element are attached at a third location on the patient;further wherein the step of receiving data at the second time furthercomprises receiving data identifying a position of the thirdlocalization element relative to the dynamic body, further comprisingthe step of determining that the third marker exists in the identifiedimage at substantially the same position as the third localizationelement relative to the dynamic body.
 4. The method of claim 1, furthercomprising: f) automatically registering a path of motion of the dynamicbody during a second time interval that includes the second time withthe path of motion of the dynamic body during the first time intervalbased on the automatically identifying for times during the second timeinterval.
 5. The method of claim 1, wherein the step of automaticallyidentifying comprises: i) determining a measurement between the positionof the first marker and the position of the second-marker in theplurality of images during the first time interval using acost-function, ii) applying the cost-function to the position of thefirst localization element and the position of the second localizationelement at the second time, iii) determining whether the position of thefirst localization element and the position of the second localizationelement at the second time is substantially the same as the position ofthe first marker and the position of the second marker, respectively, inthe images, iv) automatically computing a registration between thepositions of the first and second markers, and the positions of thefirst and second localizations elements, and v) superimposing a positionof a medical instrument on to a displayed image of the dynamic body fromthe first time interval.
 6. The method of claim 1, further comprisingproviding a navigational path for a medical instrument to be directedbased on the identified image.
 7. The method of claim 1, wherein theimages of the dynamic body are 3-D images.
 8. The method of claim 1,wherein the step of attaching the first localization element to thefirst location on the patient and the second localization element is tothe second location on the patient occurs before the first timeinterval.
 9. The method of claim 1, wherein the first localizationelement is attached to the first marker using a first connector and thesecond localization element is attached to the second maker using asecond connector.
 10. The method of claim 1, wherein the imaging deviceutilizes X-rays.
 11. The method of claim 10, wherein the step ofattaching the first localization element to the first location on thepatient and the second localization element is to the second location onthe patient occurs after the first time interval.
 12. The method ofclaim 11, wherein the localization elements comprise electro-magneticcoils in which the data identifying positions for the localizationelements is derived by measuring voltages induced in the coils.
 13. Themethod of claim 1, wherein the localization elements comprise reflectivemarkers.
 14. The method of claim 1, wherein the localization elementscomprise infrared light emitting diodes.
 15. The method of claim 1,wherein the markers are attached to the locations on the patient usingadhesive.
 16. The method of claim 15, wherein the localization elementsare attached at the locations on the patient by physically coupling thelocalization elements to the markers.
 17. The method of claim 1, whereinthe markers are attached to the locations on the patient using straps.18. The method of claim 1, wherein the markers are attached to thelocations on the patient by attaching the markers to a garment that isattached to the patient.